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8th International Conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2024

作者： Li, Meihui Song, Chunhe Yu, Xia Chu, Mengmeng Li, Rui Shenyang University of Technology State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Networked Control Systems Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang Shenyang China Shenyang University of Technology Shenyang China

ISBN: (纸本)9798331506230

Printed circuit board defect recognition is of great significance to ensuring the quality of electronic products. This paper proposes an improved PCB defect detection method based on YOLOV8. Firstly, the data augmentation method PredMix (prediction mix) is adopted to simulate scenarios such as overlapping and occlusion in images, where the displays are incomplete or blurred, thereby enhancing the robustness of the network model. Secondly, BiFPN is used to replace PAN-FPN in the fusion layer to strengthen the feature fusion capability. Finally, the CESE_C2f module is employed in the Backbone part, leveraging a lightweight attention mechanism to improve the detection effect while reducing the number of parameters. Experimental results show that compared with the original YOLOV8, the MAP value of this improved algorithm reaches 92.6%, with a 2.3% increase in accuracy. © 2024 IEEE.

关键词： Deep learning

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Working condition recognition of sucker rod pumping system based on 4-segment time-frequency signature matrix and deep learning

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Petroleum Science 2024年第1期21卷 641-653页

作者： Yun-Peng He Hai-Bo Cheng Peng Zeng Chuan-Zhi Zang Qing-Wei Dong Guang-Xi Wan Xiao-Ting Dong State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016LiaoningChina Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110016LiaoningChina Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169LiaoningChina University of Chinese Academy of Sciences Beijing100049China School of Artifcial Intelligence Shenyang University of TechnologyShenyang 110870LiaoningChina

High-precision and real-time diagnosis of sucker rod pumping system(SRPS)is important for quickly mastering oil well *** learning-based method for classifying the dynamometer card(DC)of oil wells is an efficient diagnosis ***,the input of the DC as a two-dimensional image into the deep learning framework suffers from low feature utilization and high computational ***,different SRPSs in an oil field have various system parameters,and the same SRPS generates different DCs at different ***,there is heterogeneity in field data,which can dramatically impair the diagnostic *** solve the above problems,a working condition recognition method based on 4-segment time-frequency signature matrix(4S-TFSM)and deep learning is presented in this ***,the 4-segment time-frequency signature(4S-TFS)method that can reduce the computing power requirements is proposed for feature extraction of DC ***,the 4S-TFSM is constructed by relative normalization and matrix calculation to synthesize the features of multiple data and solve the problem of data ***,a convolutional neural network(CNN),one of the deep learning frameworks,is used to determine the functioning conditions based on the *** on field data verify that the proposed diagnostic method based on 4S-TFSM and CNN(4S-TFSM-CNN)can significantly improve the accuracy of working condition recognition with lower computational *** the best of our knowledge,this is the first work to discuss the effect of data heterogeneity on the working condition recognition performance of SRPS.

关键词： Sucker-rod pumping system Dynamometer card Working condition recognition Deep learning Time-frequency signature Time-frequency signature matrix

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Dynamic compliance of energy-saving legged elastic parallel joints for quadruped robots:design and realization

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Frontiers of Mechanical Engineering 2024年第2期19卷 45-58页

作者： Yaguang ZHU Minghuan ZHANG Xiaoyu ZHANG Haipeng QIN Key Laboratory of Road Construction Technology and Equipment of Ministry of Education School of Engineering MachineryChang’an UniversityXi’an 710064China State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin 150001China

Achieving dynamic compliance for energy-efficient legged robot motion is a longstanding *** recent predictive control methods based on single-rigid-body models can generate dynamic motion,they all assume infinite energy,making them unsuitable for prolonged robot *** this issue necessitates a mechanical structure with energy storage and a dynamic control strategy that incorporates feedback to ensure *** work draws inspiration from the efficiency of bio-inspired muscle–tendon networks and proposes a controllable torsion spring leg *** design integrates a spring-loaded inverted pendulum model and adopts feedback delays and yield springs to enhance the delay effects.A leg control model that incorporates motor loads is developed to validate the response and dynamic performance of a leg with elastic *** model provides torque to the knee joint,effectively reducing the robot’s energy consumption through active or passive control *** benefits of the proposed approach in agile maneuvering of quadruped robot legs in a realistic scenario are demonstrated to validate the dynamic motion performance of the leg with elastic joints with the advantage of energy-efficient legs.

关键词： dynamic responsiveness energy dissipation legged locomotion parallel joints quadruped robot

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Relay Selection and Power control for Mobile Underwater Acoustic Communication Networks: A Dual-Thread Reinforcement Learning Approach

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年第2期9卷 1-1页

作者： Dai, Jun Li, Xinbin Han, Song Yu, Junzhi Liu, Zhixin Key Lab of Industrial Computer Control Engineering of Hebei Province China Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China

This paper deals with a cooperation communication problem (relay selection and power control) for mobile underwater acoustic communication networks. To achieve satisfactory transmission capacity, we propose a reinforcement-learning-based cooperation communication scheme to efficiently resist the highly dynamic communication links and strongly unknown time-varying channel states caused by the mobility of Autonomous Underwater Vehicles (AUVs). Firstly, a particular Markov decision process is developed to model the dynamic relay selection process of mobile AUV in the unknown scenario. In the developed model, an experimental statistical-based partition mechanism is proposed to cope with the greatly increasing dimension of the state space caused by the mobility of AUV, reducing the search optimization difficulty. Secondly, a dual-thread reinforcement learning structure with actual and virtual learning threads is proposed to efficiently track the superior relay action. In the actual learning thread, the proposed improved probability greedy policy enables the AUV to strengthen the exploration for the reward information of potential superior relays on the current state. Meanwhile, in the virtual learning thread, the proposed upper-confidence-bound-index-based uncertainty estimation method can estimate the action-reward level of historical states. Consequently, the combination of actual and virtual learning threads can efficiently obtain satisfactory Q value information, thereby making superior relay decision-making in a short time. Thirdly, a power control mechanism is proposed to reuse the current observed action-reward information and transform the multiple unknown parameter nonlinear joint power optimization problem into a convex optimization problem, thereby enhancing network transmission capacity. Finally, simulation results verify the effectiveness of the proposed scheme. IEEE

关键词： Convex optimization

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ER‐Mapping: An extrinsic robust coloured mapping system using residual evaluation and selection

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IET Cyber-systems and robotics 2024年第2期6卷 97-108页

作者： Changjian Jiang Zeyu Wan Ruilan Gao Yu Zhang State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang UniversityHangzhouChina Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province HangzhouChina

The colour-enhanced point cloud map is increasingly being employed in fields such as robotics,3D reconstruction and virtual *** authors propose ER-Mapping(Extrinsic Robust coloured Mapping system using residual evaluation and selection).ER-Mapping consists of two components:the simultaneous localisation and mapping(SLAM)subsystem and the colouring *** SLAM subsystem reconstructs the geometric structure,where it employs a dynamic threshold-based residual selection in LiDAR-inertial odometry to improve mapping *** the other hand,the col-ouring subsystem focuses on recovering texture information from input images and innovatively utilises 3D–2D feature selection and optimisation methods,eliminating the need for strict hardware time synchronisation and highly accurate extrinsic *** were conducted in both indoor and outdoor *** results demonstrate that our system can enhance accuracy,reduce computational costs and achieve extrinsic robustness.

关键词： robot vision sensor fusion simultaneous localisation and mapping(robots)

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Enhancing Robot Learning Through Cognitive Reasoning Trajectory Optimization Under Unknown Dynamics

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IEEE robotics and Automation Letters 2025年第6期10卷 5401-5408页

作者： Dong, Qingwei Wu, Tingting Zeng, Peng Zang, Chuanzhi Wan, Guangxi Cui, Shijie Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China China Mobile Research Institute Beijing100053 China Shenyang University of Technology School of Artifcial Intelligence Shenyang110870 China

In the domain of robot learning, equipping robots with the capability to swiftly acquire operational skills poses a significant challenge. Currently, reinforcement learning techniques are adept at addressing dynamic, unstructured problems involving rich contact scenarios. However, the convergence rate of these algorithms is often slow due to the high dimensionality of the robot state-action mapping space and the extensive initial policy search space. Meanwhile, advancements in large language models (LLMs) have endowed these models with a degree of logical reasoning ability, enabling them to take goal-oriented actions proactively during the initial phase of a robotic task. These models can implicitly generate features of states and uncover underlying patterns in trajectory generation. Yet, in complex manipulative tasks involving rich contact scenarios, LLMs still fall short. Thus, integrating the robust interactive capabilities of reinforcement learning with the strong logical reasoning of LLMs, and enhancing policy search with LLMs, could potentially accelerate the speed of policy searches significantly. In this letter, we introduce a Cognitive Reasoning Trajectory Optimization method. This approach utilizes Low-level Cognitive control Tuning to enable LLMs with robust logical reasoning to make effective single-step decisions in Markov Decision Process (MDP) tasks. By fitting dynamic models with high-quality cognitive reasoning data and optimizing control strategies, this method constrains the policy search space and enhances the efficiency of trajectory optimization. Experimental results on various manipulative tasks using the Sawyer robot in the Mujoco simulator validate the effectiveness of the proposed algorithm. © 2016 IEEE.

关键词： Markov processes

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A practical PID variable stiffness control and its enhancement for compliant force-tracking interactions with unknown environments

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Science China(Technological Sciences) 2023年第10期66卷 2882-2896页

作者： ZHANG Xin ZHOU Hao LIU JinGuo JU ZhaoJie LENG YuQuan YANG ChenGuang State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China University of Chinese Academy of Sciences Beijing 100049China School of Computing University of PortsmouthPortsmouth PO13HEUK Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Department of Mechanical and Energy EngineeringSouthern University of Science and TechnologyShenzhen 518055China Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and EngineeringSouth China University of TechnologyGuangzhou 510640China

Compliant interaction control is a key technology for robots performing contact-rich manipulation *** design of the compliant controller needs to consider the robot hardware because complex control algorithms may not be compatible with the hardware performance,especially for some industrial robots with low bandwidth *** paper focuses on effective and easy-to-use compliant control algorithms for position/velocity-controlled *** by human arm stiffness adaptation behavior,a novel variable target stiffness(NVTS)admittance control strategy is proposed for adaptive force tracking,in which a proportional integral derivative(PID)variable stiffness law is designed to update the stiffness coefficient of the admittance function by the force and position ***,its stability and force-tracking capability are theoretically *** addition,an impact compensator(Impc)is integrated into the NVTS controller to enhance its disturbance-suppression capability when the robot is subjected to strong vibration disturbances in complicated surface polishing *** proposed controllers are validated through four groups of experimental tests using different robots and the corresponding results demonstrate that they have high-accuracy tracking capability and strong adaptability in unknown environments.

关键词： compliant interaction control force tracking PID variable stiffness impact compensator unknown environments

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An intelligent matching method for the equivalent circuit of electrochemical impedance spectroscopy based on Random Forest

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材料科学技术（英文版） 2025年第6期209卷 300-310页

作者： Wenbo Chen Bingjun Yan Aidong Xu Xin Mu Xiufang Zhou Maowei Jiang Changgang Wang Rui Li Jie Huang Junhua Dong Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110169China Shenyang Institute of Automation Chinese Academy of SciencesShenyang 110169China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang 110169China Shenyang Institute of Automation Chinese Academy of SciencesShenyang 110169China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang 110169China Shenyang National Laboratory for Materials Science Institute of Metal ResearchChinese Academy of SciencesShenyang 110016China General Research Institute China Oil & Gas Pipeline Network CorporationLangfang 065000China

One of the core works of analyzing Electrochemical Impedance Spectroscopy(EIS)data is to select an appropriate equivalent circuit model to quantify the parameters of the electrochemical reaction ***,this process often relies on human experience and judgment,which will introduce subjectivity and *** this paper,an intelligent approach is proposed for matching EIS data to their equivalent circuits based on the Random Forest *** can automatically select the most suitable equivalent circuit model based on the characteristics and patterns of EIS *** the typical scenario of metal corrosion,an atmospheric corrosion EIS dataset of low-carbon steel is constructed in this paper,which includes five different corrosion *** dataset was used to validate and evaluate the pro-posed method in this *** contributions of this paper can be summarized in three aspects:(1)This paper proposes a method for selecting equivalent circuit models for EIS data based on the Random Forest algorithm.(2)Using authentic EIS data collected from metal atmospheric corrosion,the paper es-tablishes a dataset encompassing five categories of metal corrosion scenarios.(3)The superiority of the proposed method is validated through the utilization of the established authentic EIS *** ex-periment results demonstrate that,in terms of equivalent circuit matching,this method surpasses other machine learning algorithms in both precision and ***,it shows strong applicability in the analysis of EIS data.

关键词： Electrochemical impedance spectroscopy Random forest Corrosion Equivalent circuit model

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A Model Integrating Wavelet Convolution and Linear Attention Mechanism for Inventory Management in Power Warehouses 4

A Model Integrating Wavelet Convolution and Linear Attention...

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4th IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Li, Tao Zhang, Zhengli Li, Ligang Du, Guozheng State Grid Shandong Electric Power Company Material Company Jinan China Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang China Chinese Academy of Sciences Key Laboratory of Networked Control Systems Shenyang China

ISBN: (纸本)9798350363586

Accurate inventory management in power warehouses is crucial for maintaining consistency between records and actual stock levels. However, the complex storage layout and the diversity of stored materials often lead to human errors during manual inventory checks, resulting in inefficiencies. To address these challenges, we collected 2,000 images from power warehouse environments and created a binary classification dataset containing images of correctly and incorrectly placed items. We propose a novel network, CWAMNet, specifically designed for visual inventory management in complex power warehouse environments. CWAMNet integrates wavelet convolution and a linear attention mechanism to overcome the limitations of traditional convolutional neural networks (CNNs) and standard attention mechanisms, achieving a balance between model capacity and computational efficiency. © 2024 IEEE.

关键词： Convolutional neural networks

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Trajectory control of Multi-Axis Robotic Arms Based on Physics-Informed Neural Networks and Nonlinear Model Predictive control

Trajectory Control of Multi-Axis Robotic Arms Based on Physi...

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2024 China Automation Congress, CAC 2024

作者： Wang, Yiqing Xia, Changqing Jin, Xi Xu, Chi Zhang, Yanzhu School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350368604

Multi-axis robotic arms are extensively utilized in intelligent manufacturing scenarios, with trajectory control in flexible scenarios constituting a primary challenge. Physics-Informed Neural Networks (PINNs) represent advanced methods that integrate physical laws with data-driven approaches. Despite their outstanding performance in integrating theory and data, research on their application for controlling robotic arms in complex scenarios remains limited. This paper establishes a nonlinear dynamic model based on the physical relationships between the axes of robotic arms. In the absence of prior data, Sobol sequence random sampling is employed to generate data, which are subsequently trained using PINNs. Considering system noise, the Extended Kalman Filter (EKF) is utilized to predict the next state in noisy environments, and Nonlinear Model Predictive control (NMPC) is implemented to control the robotic arm for trajectory tracking, achieving real-time control. Simulation results demonstrate that the proposed Discrete Physics-Informed Predictive control (DPIPC) method exhibits smaller position and velocity errors with less fluctuation compared to the method in the reference, indicating superior control capabilities. © 2024 IEEE.

关键词： Extended Kalman filters

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